Low Input Tree Breeding Strategies

1. Low Input Tree Breeding Strategies Dag Lindgren Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences, Umeå Sweden September 2, 2002, China

2. Scientists’ focus: high input Output high-input-strategy Input high-input low-input Because of training and ambitions the attention is mostly here

3. High-input techniques • Genotype testing, • Controlled crosses, • Known pedigrees, • Orchards intensively managed exclusively for seed production, • Grafts for seed production.

4. High input • Scientific rewards and fancy journal papers, • Collegues’ admiration, • Non-sloppy well organised programs, • The main driving force of human phsyc, • Focus for teaching in schools and textbooks at Universities.

5. High input and low input high-input-strategy Output low-input-strategy Input low-input high-input Attention should be here for low-input strategy

6. Low input situations • Poor • Unstable organisation • Local control • No specialists • Minor program • Lower tiers of breeding population (main, but not elite)

7. Low-input techniques • Selection on phenotypes instead of testing of genotypes, • No records of tree ID or pedigree, • Wind pollination, • Seed production in stands used for other purposes, • “Cheap” plantations created for future seed production and long term improvement.

8. Low-input techniques • Thin stands rather intense to get better pollen and take seeds from best trees But…Depend on predictions of inbreeding, coancestry and diversity replacing pedigree. Predictions may fail…. and are generally not even made yet. Note that there is no strict limit between high input and low input techniques!

9. Phenotypic selection • No tree identities required, • No computer required, • No strict objective measures required, • Transparent (no black box) • Can be executed immediately in field, • A type of selection forwards, • Can also be called mass-selection, • Similar to how Nature selects, thus sustainable and environmental friendly.

10. Phenotypic selection • Depending on predictions for control of accumulation of relatedness, • Can be diversity use efficient,

11. 0.5 0 0 0.5 1 Maximising gain at a given diversity by selection in infinite normal distributions. h2=0.25 and P=0.1 Combined index=estimated BV(maximizes gain) Phenotypic selection (easy) Between family(exhausts diversity) Gain Within family(preserves diversity) Diversity Note that phenotypic selection is on the optimising curve, thus no other selection results in higher gain without sacrifying diversity! Modified From Lindgren and Wei 1993

12. Testing is doubtful for low-input breeding • more complicated, • more demanding on temporal and organisational stability, • a considerable long time investment requires trust on that the results would be used, • selection forward (untested) is often found to offer as high gain as selection backwards (means progeny-test doubtful effectiveness), • Progeny-testing often not competitive.

13. 30 Phenotypic 24 Breeding valueestimate Gain 18 12 4 6 8 10 12 14 Effective number (Ns) Restricted selection for Phenotypic and Breeding value (combined index, conciders both individual and family) in a population created by 20 parents with family size 20, h2=0.5. Points correspond to restriction intensity. Simulation (POPSIM).

14. 30 Phenotypic Unrestrictedbreeding valueestimate 24 Gain Balancedselection 18 12 4 6 8 10 12 14 Effective number (Ns)

15. Note: • Phenotypic selection as good as restricted selection for breeding value compared at same gene diversity! • Unrestricted breeding value selection gives a higher gain, but at the cost of a lower gene diversity! • Remember: Breeding value is based on a combined index of an individual and its sibs used for selection forwards. It does not refer to parental ranking.

16. Restricted selection for Phenotypic and Breeding value for several generationsOne and five generations of selection in a population with a family structure, h2=0.5, family size 20 Breeding value Phenotype 110 100 5 generations 90 80 70 Gain 60 50 40 30 20 1 generation 10 0 0.1 0.2 0.3 0.4 0.5 Loss of gene diversity

17. Note • Phenotypic selection is compatible also in a multigeneration program • For unrestricted selection for breeding value the genetic variation get exhausted after a number of generations, and in the long run the gain than with phenotypic selection is higher • However, if breeding population large and heritability not very large, this exhaustion takes long time.

18. One and five generations of restricted selection in a population with a family structure, h2=0.05, family size 500. Low heritability and large families favor combined index Phenotype Breeding value 40 5 generations 30 Gain 20 10 1 generation 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 Loss of gene diversity

19. Development of Gain and gene diversity over five generations of selection in a population with a family structure, h2=0.05, family size 500 for three selection strategies. 40 Unrestricted breeding value After five generations 30 Phenotypic Gain 20 Balanced 10 After first generation 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 Loss of gene diversity

20. Selection strategies

21. 0.07 Female and male varies 0.06 Female constant 0.05 Equal-tree fertility 0.04 Coancestry (inbreeding) 0.03 0.02 0.01 0.00 1 2 3 4 5 6 7 8 9 10 Generations Fertility variation matters for accumulation of relatedness over generations The development over generations in a closed population of 154 teak trees based on their observed fertility variations (Bila et al. 1999)

22. Grafted seed orchards not low-intensity • large investment, • long-term investment, • trust in use of product, • large scale (management, pollen), • special technical competence required, • particular land use, • organisational stability over time required, • selected fathers, but....

23. “Gene resource plantation” • Looks and is managed similar to a "normal" plantation, • Limited need of specialised competence and organisational stability, • Multiple use (options for seeds and improvement, wood, conservation...), • Can function as seed collection area, • As “cheap” trees may even be cut for seed collection, • Can be close to local organisation, enterprise and people,

24. Gene resource plantation • Robust to disasters or neglectance, • Small investment, thus limited loss if interest lost • Optional plant identity, but…, • Seeds for commercial use and replacing can be improved by thinning and by harvesting the best trees, • Renewed by wind-pollinated seeds mainly from the plantation, • Coancestry controlled by predictions, sufficient numbers and seed parent contribution control.

25. Will "we" be needed? • Need to optimise "low-intensity" strategy and tactics (almost not done yet) • Less control (wind-pollination, unidentified trees etc.) means much more thought and predictions needed • The reduced need of competence is "technical competence and field competence“. Not competence of breeding scientists • Low-input should replace No-input, not High-input